A New Well Drilling Monitoring System Based on Field Bus and Expert System

2006 ◽  
Author(s):  
Wei Zhang ◽  
Hongzan Bin ◽  
Yongshou Dai
Keyword(s):  
Expert System ◽  
Monitoring System ◽  
Field Bus ◽  
Well Drilling

The Design of Remote Monitoring System and Diagnosing System in Natural Gas Compressor

2014 ◽  
Vol 530-531 ◽  
pp. 301-305
Author(s):  
Huan Huan Nie ◽  
Zhen Lu Wu ◽  
Guo Yan Yu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Natural Gas ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Frame Structure ◽  
Inference Rules ◽  
Remote Monitoring System ◽  
Fault Inference ◽  
A Company

The frame structure of monitoring system is introduced and the network structure of monitoring system is analyzed in this paper. Fault diagnosis expert system is put forward and the fault inference rules are elaborated according to the fault of natural gas compressor of a company. The significance of the system is summarized in the end.

scholarly journals An Expert System for Trend Analysis Applied to Turbomachinery

1991 ◽  
Author(s):  
Thomas Van Hardeveld
Keyword(s):  
Expert System ◽  
Trend Analysis ◽  
Monitoring System ◽  
Health Monitoring ◽  
Diagnostic Techniques ◽  
Statistical Techniques ◽  
Health Monitoring System ◽  
Monitoring Techniques ◽  
Computerized Monitoring ◽  
Automated Methods

With the advent of computerized monitoring techniques, it is becoming evident that more automated methods of trend analysis and other diagnostic techniques are both possible and necessary. The implementation of a computerized health monitoring system has led to research into techniques for identifying trend behavior which can be used to detect equipment deterioration. The result has been the development of statistical techniques to characterize generic trend behavior and of an expert system to translate these into a diagnosis of equipment deterioration.

Knowledge-based interpretation of seismic data in the Intelligent Monitoring System

1993 ◽  
Vol 83 (5) ◽  
pp. 1507-1526 ◽  
Author(s):  
Thomas C. Bache ◽  
Steven R. Bratt ◽  
Henry J. Swanger ◽  
Gregory W. Beall ◽  
Frederick K. Dashiell
Keyword(s):  
Expert System ◽  
Bayesian Analysis ◽  
Monitoring System ◽  
High Frequency ◽  
Distributed Processing ◽  
Neural Nets ◽  
Compact Representation ◽  
Seismic Events ◽  
Automated Identification ◽  
Intelligent Monitoring

Abstract The Intelligent Monitoring System (IMS) provides a new capability for automated and interactive analysis of data to detect and locate seismic events recorded by a network of seismic stations. IMS integrates emerging technologies in artificial intelligence, database management, computer graphics, and distributed processing into an operational system used for routine bulletin production and associated research tasks. The first version of IMS (Bache et al., 1990a,b; Bratt et al., 1990) was designed for detection and location of regional events recorded by the two high-frequency arrays in Norway (NORESS and ARCESS). This has been extensively revised and expanded to become IMS Version 2 which is designed to detect and locate all seismic events recorded by an arbitrary seismic network. Since March 1991 it has been operated continuously to process the data from four high-frequency arrays (adding FINESA in Finland and GERESS in Germany). For some periods data from as many as seven 3-component stations in Eurasia have also been included in the processing. The most important new element is ESAL (Expert System for Association and Location) which interprets signal detections to form and locate seismic events. It is programmed in the ART expert system shell which provides the knowledge representation framework and inference mechanisms for complex and knowledge-rich rule-based reasoning. The current version of ESAL represents knowledge through approximately 200 ART rules that are configured through about 300 user-specified parameters and tables. The IMS architecture and operational procedures are designed to facilitate acquisition of new knowledge for ESAL. Knowledge acquisition methods being used include: Bayesian analysis, training neural-nets, statistical analysis to estimate parameters configuring rules, computing fuzzy-logic membership functions, and formulating new rules. Only the Bayesian probabilities are discussed in detail here. They provide a compact representation of complex knowledge about station-specific differences in phase characteristics. As an example, we describe the rules used for automated identification of detected regional Sn, Lg, and Rg phases. Using a Bayesian analysis technique, we quantify the differences in S-phase characteristics. The data show that they fall into two classes with GERESS distinct from the three Fennoscandia arrays.

Research on General Remote Monitoring System for Industrial Equipment

2012 ◽  
Vol 201-202 ◽  
pp. 678-681
Author(s):  
Dong Ming Xu ◽  
Li Sheng Shu
Keyword(s):  
Data Acquisition ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Field Data ◽  
Internet Technology ◽  
Industrial Equipment ◽  
Field Bus ◽  
Remote Monitoring System ◽  
System Maintenance ◽  
Data Acquisition Unit

A general embedded remote monitoring system for industrial equipment has been designed based on Field Bus Technology and Internet Technology. The Field Bus includes PROFIBUS, CAN, RS-485 and RS-232. The data of the device controlled in the system can be detected and transmitted to PC via Internet. Upper monitor in the system is remote PC. Console computer, which can communicate with industrial equipment controller by Field Bus, is the controller of field data acquisition unit. Two working modes can be realized in the system. One is the data of operating status, which is gotten by Field Bus before transferred to PC. The other is the data of operating status, which is detected by field data acquisition unit in remote monitoring system. Maintenance and management becomes convenient after applying the remote monitoring system.

An Intelligent Monitoring System Using a ZigBee-Base Sensor Network

2012 ◽  
Vol 182-183 ◽  
pp. 1393-1401
Author(s):  
Kun Yung Lu
Keyword(s):  
Wireless Sensor Network ◽  
Expert System ◽  
Sensor Network ◽  
Monitoring System ◽  
Monitoring Network ◽  
Wireless Sensor ◽  
Network Configuration ◽  
Intelligent Monitoring ◽  
Expert System Shell ◽  
Configuration Module

This paper presents an intelligent monitoring system based on a ZigBee-base wireless sensor network. The proposed system includes a sensor network configuration module, an expert system shell, and an event-condition-action (ECA) engine. The sensor network configuration module is used to configure the working properties of the ZigBee components and set up the monitoring network. The expert system shell enables users to define the related events occurred at system running, the threshold of working conditions for triggering the corresponding events, and the required commands for fitting the system. The ECA engine is used to monitor the system and provide a suitable command for system fitting in a specified event being fired. The proposed system enables user to quickly establish a wireless sensor network system without well-experienced knowledge about the wireless sensor network and the expert system.

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